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Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China

Author

Listed:
  • Rizwan Muhammad

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

  • Wenyin Zhang

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

  • Zaheer Abbas

    (School of Geography, South China Normal University, Guangzhou 510631, China)

  • Feng Guo

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

  • Luc Gwiazdzinski

    (Institut de Géographie Alpine (IGA), Université Grenoble Alpes, 38100 Grenoble, France)

Abstract

Land use and land cover (LULC) change analysis is a systematic technique that aids in the comprehension of physical and non-physical interaction with the natural habitat and the pursuit of environmental sustainability. Research regarding LULC’s spatiotemporal changing patterns and the simulation of future scenarios offers a complete view of present and future development possibilities. To simulate the spatiotemporal change transition potential and future LULC simulation, we utilized multi-temporal remotely sensed big data from 1990 to 2020 with a 10-year interval. Independent variables (DEM, slope, and distance from roads) and an integrated CA-ANN methodology within the MOLUSCE plugin of QGIS were utilized. The findings reveal that physical and socioeconomic driving variables have a substantial effect on the patterns of the terrain. In the last three decades, the study area had a significant rise in impervious surface from 10.48% to 26.91%, as well as a minor increase in water from 1.30% to 1.67%. As a result, forest cover decreased from 12.60% to 8.74%, green space decreased from 26.34% to 16.57%, and barren land decreased from 49.28% to 46.11%. Additionally, the predictions (2030–2050) support the increasing trend towards impervious surface at the expense of significant quantities of forest and green space.

Suggested Citation

  • Rizwan Muhammad & Wenyin Zhang & Zaheer Abbas & Feng Guo & Luc Gwiazdzinski, 2022. "Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China," Land, MDPI, vol. 11(3), pages 1-24, March.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:3:p:419-:d:770565
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    References listed on IDEAS

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    2. Zaheer Abbas & Guang Yang & Yuanjun Zhong & Yaolong Zhao, 2021. "Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China," Land, MDPI, vol. 10(6), pages 1-26, June.
    3. Zihao Zheng & Zhifeng Wu & Yingbiao Chen & Zhiwei Yang & Francesco Marinello, 2020. "Detection of City Integration Processes in Rapidly Urbanizing Areas Based on Remote Sensing Imagery," Land, MDPI, vol. 9(10), pages 1-15, October.
    4. Lang, Wei & Long, Ying & Chen, Tingting, 2018. "Rediscovering Chinese cities through the lens of land-use patterns," Land Use Policy, Elsevier, vol. 79(C), pages 362-374.
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